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Hierarchy evolution for improved classification

Authors:

Xiaoguang Qi,

Brian D. DavisonAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2193 - 2196

https://doi.org/10.1145/2063576.2063924

Published: 24 October 2011 Publication History

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Abstract

Hierarchical classification has been shown to have superior performance than flat classification. It is typically performed on hierarchies created by and for humans rather than for classification performance. As a result, classification based on such hierarchies often yields suboptimal results. In this paper, we propose a novel genetic algorithm-based method on hierarchy adaptation for improved classification. Our approach customizes the typical GA to optimize classification hierarchies. In several text classification tasks, our approach produced hierarchies that significantly improved upon the accuracy of the original hierarchy as well as hierarchies generated by state-of-the-art methods.

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Cited By

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Zhang XWang ZGong YWang CYang JYuan HDu H(2023)A Novel Hierarchical Storage System for Different Types of DataProceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 210.1007/978-981-99-1157-8_84(700-709)Online publication date: 1-Apr-2023
https://doi.org/10.1007/978-981-99-1157-8_84
Naik ARangwala HNaik ARangwala H(2018)Hierarchical Structure InconsistenciesLarge Scale Hierarchical Classification: State of the Art10.1007/978-3-030-01620-3_3(39-59)Online publication date: 10-Oct-2018
https://doi.org/10.1007/978-3-030-01620-3_3
Gupta VGupta KBhavsar ASao A(2016)Hierarchical classification of HEP-2 cell images using class-specific features2016 6th European Workshop on Visual Information Processing (EUVIP)10.1109/EUVIP.2016.7764585(1-6)Online publication date: Oct-2016
https://doi.org/10.1109/EUVIP.2016.7764585
Show More Cited By

Index Terms

Hierarchy evolution for improved classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2011

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CIKM '11

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CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Zhang XWang ZGong YWang CYang JYuan HDu H(2023)A Novel Hierarchical Storage System for Different Types of DataProceedings of the 4th International Conference on Big Data Analytics for Cyber-Physical System in Smart City - Volume 210.1007/978-981-99-1157-8_84(700-709)Online publication date: 1-Apr-2023
https://doi.org/10.1007/978-981-99-1157-8_84
Naik ARangwala HNaik ARangwala H(2018)Hierarchical Structure InconsistenciesLarge Scale Hierarchical Classification: State of the Art10.1007/978-3-030-01620-3_3(39-59)Online publication date: 10-Oct-2018
https://doi.org/10.1007/978-3-030-01620-3_3
Gupta VGupta KBhavsar ASao A(2016)Hierarchical classification of HEP-2 cell images using class-specific features2016 6th European Workshop on Visual Information Processing (EUVIP)10.1109/EUVIP.2016.7764585(1-6)Online publication date: Oct-2016
https://doi.org/10.1109/EUVIP.2016.7764585
Yuan QCong GSun ALin CThalmann NHersh WCallan JMaarek YSanderson M(2012)Category hierarchy maintenanceProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348389(791-800)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2348283.2348389

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